微波湿度计条带噪声的分析与抑制方法研究  

Striping noise analysis and mitigation for microwave humidity sounder

作　　者：刘明绪 张升伟 何杰颖 LIU Mingxu;ZHANG Shengwei;HE Jieying(Key Laboratory of Microwave Remote Sensing,National Space Science Center,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院国家空间科学中心微波遥感重点实验室,北京100190 [2]中国科学院大学,北京100049

出　　处：《遥感学报》2023年第10期2318-2326,共9页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家重点研发计划(编号:2018YFB0504900,2018YFB0504902)。

摘　　要：本文基于主成分分析PCA(Principal Components Analysis)和集合经验模态分解EEMD(Ensemble Empirical Mode Decomposition)降噪的基本思想,利用改进的自适应噪声总体集合经验模态分解ICEEMDAN(Improved Complete Ensemble Empirical Mode Decomposition With Adaptive Noise)对原EEMD的条带降噪方法进行了修改,并利用该方法对风云三号C星、D星(FY-3C、FY-3D)微波湿度计(MWHS-2)实际观测亮温中的条带噪声进行了分析。其中主成分分析对数据进行降维,得到各扫描线的主成分分量,模态分解方法分解对应分量,利用各模态能量密度的差异提取出其中的噪声并去除,随后组合剩余模态重构出观测亮温,实现噪声的抑制。通过对原始算法和各类改进后的模态分解方法降噪效果的对比,结果表明使用ICEEMDAN可有效避免EEMD中残余噪声等问题,减少重构误差。数值分析结果表明,改进后的方法使方差进一步降低0.020 K2,信噪比提升0.031 dB,进一步提升了算法的降噪能力。Noise analysis and mitigation play an important role in meteorological satellite data processing.This study is based on the idea of noise mitigation by using Principal Component Analysis(PCA),Ensemble Empirical Mode Decomposition(EEMD)algorithm,and improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN).The modified method is used to observe data of the microwave humidity sounder(MWHS-2)of the Fengyun-3C and 3D satellites(FY-3C,FY-3D)to analyze the striping noise in its observed brightness temperature.In this study,the effectiveness of this method for MWHS-2 data is confirmed,and a performance analysis of the improved method for data processing and noise mitigation is conducted.The striping noise has a very high correlation with scan line;thus,using PCA can not only effectively isolate the noise-related principal components,but also reduce the dimension of the processed data.When the noise containing principal components is extracted,the empirical mode decomposition method can be used to adaptively separate each component into multiple modes with different frequencies.The noise can be easily separated from the signal by a method that calculates and compares the average period and energy density by using the differences in energy between noise and signal modes.Finally,the remaining modes are combined to reconstruct the principal components,which reconstruct the observed brightness temperature data.When this method is applied to the MWHS-2 data,we used the hourly global reanalysis data of ERA5 with RTTOV model to generate the simulated brightness temperature data and compared with the observed brightness temperature before and after processing.The result shows that the algorithm successfully extracts the striping noise in the signal,and the noise histogram exhibits a Gaussian distribution.The noise mitigation effect between the original EEMD algorithm and various improved mode decomposition methods is compared,and the results show that the use of ICEEMDAN can effectively avoid some pro

关 键 词：风云三号 MWHS-2 微波辐射测量 数据处理 降噪 PCA 经验模态分解 条带噪声 

分 类 号：TP701[自动化与计算机技术—检测技术与自动化装置] P2[自动化与计算机技术—控制科学与工程]